Method for Determining the Probability of a Collision of a Vehicle With a Living Being

ABSTRACT

The invention describes a method for determining the probability of a collision of a vehicle with a living being, in which the behaviour in space and time of the living being is modelled by means of a behavioural model and the behaviour in space and time of the vehicle is modelled by means of a kinematic model and, starting from the current positions of the vehicle and the living being, at least one trajectory for each of them is determined. According to the invention, the current positions of the living being and of the vehicle are used to compute trajectories of the vehicle and of the living being as a trajectory pair until said trajectory pair either indicates a collision or indicates no collision, whereupon the number of trajectory pairs indicating a collision is determined, and the probability of a collision is determined as the quotient of the number of trajectory pairs indicating a collision and the total number of trajectory pairs that have been computed.

The invention relates to a method for determining the probability of acollision of a vehicle with a living being, in particular a pedestrian,according to the preamble of patent claim 1, in particular for use in aperson protection system in a vehicle or a driving simulator.

In such a method, surroundings information is obtained by means of atleast one sensing system. Said surroundings information is evaluated bya computing unit in order to identify a living being. Furthermore, amovement trajectory and a state of motion are determined for the livingbeing on the basis of a behavioural model of said living being at acertain moment in time in order to assess the probability of acollision, for example of a pedestrian with the vehicle.

A high risk of a collision, i.e. a high probability of a collision, canlead to various actions to protect the pedestrian. For example, awarning can be issued to the driver and/or the pedestrian, a pedestrianprotection device can be activated, or autonomous vehicle actions, suchas for example an emergency braking or an emergency steering manoeuvre,can be carried out.

In order to detect the risk of a collision between motor vehicles andpedestrians, cyclists or animals (in general living beings) in roadtraffic and to initiate appropriate protective measures if theprobability of a collision is high, relevant traffic situations must berecorded and evaluated. This information can be used to determine astate of motion of the vehicle on the one hand and a state of motion ofthe living being that is observed on the other. The further movementbehaviour of the two road users is determined by extrapolation.

To identify collision situations and to differentiate correctly betweencritical and non-critical situations, high-quality methods to calculatethe existing risk must be used.

For example, it is known to perform a risk assessment exclusively on thebasis of a statistical analysis of the error variances of the positionsof the pedestrian that have been determined, or as an alternative, tobase the calculation on the assumption of a one-dimensional transversedistribution of the areas occupied by the vehicle and the pedestrian andto use the mathematical operation of convolution of the two transversedistributions.

The ability to predict the movement behaviour of the living being isalso crucial for the reliability of the collision risk assessment. Themore precise said prediction ability is, the better protection systemswhich are adapted to the situation can be selected and activated. Inparticular, this also serves to avoid false activations which do notcontribute to protecting the road users but rather increase themaintenance costs of the vehicle or confuse the driver or causesecondary damage in the case of false warnings.

DE 103 25 762 A1 describes a method for operating an image processingsystem for a vehicle. In said method, surroundings information isobtained by means of at least one image sensor and evaluated by acomputing unit in order to detect the presence of road users. Amongother parameters, the gaze direction of one or several road users thathave been identified is detected. The risk of a collision is assessedtaking into account the attentiveness of the road users. The gazedirection of one or several road users serves as an indicator ofattentiveness. This is based on the consideration that the gazedirection of a road user indicates whether said road user is attentiveand e.g. notices an approaching vehicle. The risk of a collision isassessed to be higher if the road user gazes in a direction facing awayfrom the image sensor and to be smaller if said road user gazes directlyinto the image sensor. In addition, it is intended to create aprobability scale for collision risk assessment, based on the detectionand evaluation of the gaze directions of road users that have beenidentified. This is done using motion information of the vehicle and/orof the road user(s) that have been detected.

Said motion information includes the speed, direction and trajectory ofmovement of a vehicle and/or a road user that has been identified.

In addition, EP 1 331 621 B1 discloses a method for monitoring thesurroundings of motor vehicles with regard to the risk character of apotential obstacle, wherein the uncertainty of position measurements aswell as the uncertainties in respect of the future behaviour of theobject are taken into account, in particular including special andsudden events which are liable to change the future behaviour of saidobject. To determine the probability of a collision, the maximum areawhich can be reached by the object is determined at subsequent momentsin time. The result is a trajectory path which becomes wider and widerin the direction of future moments in time. The probability of acollision at a particular moment in time is then determined by thepercentage overlap of the areas defined by the potential positions ofthe vehicle and of the object at this moment in time. If said areas donot overlap, the probability of a collision is zero; if there is acomplete overlap, said probability is 100%. The drawback of this knownmethod is that the future behaviour of the object is based on abehavioural model which only includes kinematic parameters, such asdirection, speed and acceleration, and extrapolates them into thefuture.

It is therefore the object of the present invention to avoid thedrawbacks of the state of the art and to provide a method fordetermining the probability of a collision of a vehicle with a livingbeing.

The aforesaid object is achieved by means of a method having thefeatures of claim 1. Advantageous further developments are set forth inthe dependent patent claims.

In the method according to the invention for determining the probabilityof a collision, the current positions of the living being and of thevehicle are used to compute trajectories of the vehicle, based on thekinematic model, and of the living being, based on the behaviouralmodel, as a trajectory pair until said trajectory pair either indicatesa collision or indicates no collision. Subsequently, the number oftrajectory pairs indicating a collision is determined and used tocompute the probability of a collision as the quotient of the number oftrajectory pairs indicating a collision and the total number oftrajectory pairs that have been computed.

In this way, the probability of a collision, hereinafter also referredto as collision risk value, is computed as a relative collisionfrequency, i.e. as a ratio of the number of vehicle/living beingtrajectory pairs where a collision would occur to the total number ofpotential vehicle/living being trajectory pairs that have been computed.

In a further development of the invention, a collision is indicated ifthe distance between the vehicle and the living being which is indicatedby the trajectories of a trajectory pair is below a predefinedthreshold. Such a distance threshold is preferably adapted to thedimensions of human beings; for example, the radius of the circumcirclearound the contour of a pedestrian as seen from above would be suitablefor this purpose.

It is particularly advantageous if the method steps of

b) using the current positions of the vehicle and of the living being tocompute trajectories of the vehicle and of the living being as atrajectory pair until said trajectory pair either indicates a collisionor no collision is indicated,c) determining the number of trajectory pairs indicating a collision,andd) computing the probability of a collision as the quotient of thenumber of trajectory pairs indicating a collision and the total numberof trajectory pairs that have been computed are repeated at timeincrements.

This shows the development of the risk of a collision during the courseof the scenario between the vehicle and the living being or thepedestrian over time, so that the chronological development of theprobability of a collision or of the collision risk value is obtained asa result. Said collision risk value can be used to activate pedestrianprotection systems if it exceeds a predefined threshold, wherein saidactivation may in addition be dependent on the development of thecollision risk value.

In a further development of the invention, the behavioural model is usedto determine potential positions of the living being at one or severalmoments in time, taking into account the state of motion at the timewhen the computation of a trajectory pair starts.

To determine the potential future position at a given moment in time,the behavioural model for the behaviour of the living being in space andtime is applied to a place of the movement trajectory and the state ofmotion, thus determining potential positions at one or several futuremoments in time.

Moreover, in a particularly preferred further development of theinvention, the computation of the trajectories of the living being isbased on a behavioural model which takes into account the physical andphysiological movement ability of the living being and/or behaviouralpatterns that have been determined empirically, i.e. it is assumed thatthe living being, due to his/her physiology, cannot move in alldirections with the same acceleration ability and, in addition, may havecertain preferred directions due to his/her general behaviour. Incontrast to conventional trajectory algorithms, the method does notproject the current mode of movement into the future, but uses it as abasis while taking into account a limited physiological movement abilityand/or preferred movements which are due to the general behaviour of theliving being. In addition, living beings or pedestrians differ from theother usual objects in road traffic in that they are able to make suddenchanges in direction by rotating about their own axis, by sideways orbackward steps, thus changing the position of the living beingdramatically compared to conventional trajectory predictions, as hasbeen found in various motion studies.

In the description below, “living being” means a cyclist, a pedestrianor an animal. A “position” of the living being is understood as an areawhere said living being will very probably be located at a future ornext moment in time (with a probability of more than 50%, in particularmore than 70%, and even more preferred more than 90%).

The recording of surroundings information by means of sensors, forexample using imaging methods, serves to determine a movement trajectoryon the one hand and a state of motion of the living being on the other.If both these pieces of information are then combined with thephysiological movement ability of the living being, which takes intoaccount biomechanical facts and/or behaviour-specific preferreddirections of the living being that has been detected, potentialpositions at one or several future moments in time can be determinedwith greater accuracy. This information can then be used to compute theprobability of a collision.

The sensing system used to obtain the surroundings information cancomprise for example radar, LiDAR, cameras, ultrasonic sensors, or beconstituted or supported by communication technologies, such as e.g.RFID (RFID=Radio Frequency Identification) or GPS (GPS=GlobalPositioning System).

One or several of the parameters below are determined and processed asparameters for the determination of the state of motion and/or of thepotential future position:

-   -   A position of the living being. This means in particular a        relative position of the living being to the vehicle. The        criterion can also be a distance or a relative position of said        living being from or to a path of movement of the vehicle that        has been determined.    -   An orientation of the living being relative to the surroundings.        This means in particular the angle at which the living being is        positioned relative to the surroundings, in particular to the        vehicle or a road. Due to the physiological movement ability of        the living being, the orientation of said living being relative        to the surroundings, e.g. positioned with his/her back to the        road or the vehicle or walking with his/her side to the road or        the vehicle, is of great importance for the potential future        position.    -   A translational and/or rotational speed of the living being. The        physiological movement ability and hence the potential future        position depend on the speed of the living being, i.e. on how        fast said living being moves.    -   A translational and/or rotational acceleration of the living        being, which, due to the physiological movement ability of said        living being, determines the maximum speed that can be achieved        by said living being and/or the further acceleration ability.    -   A current radius of curvature of the movement made by the living        being and/or a change in a direction of movement or of a radius        of curvature of the movement of the living being. This parameter        to be taken into account is based on the consideration that a        living being that is moving in a curve has a limited capability        to change his/her direction of movement and/or speed and/or        acceleration, compared to a living being that walks in a        straight line.    -   A ground friction coefficient of the road surface, which in        particular depends on the weather and can be scaled, e.g. if        said surface is found to be wet. The ground friction coefficient        is of decisive importance for the acceleration ability of the        living being.    -   A class the living being belongs to, in particular the age of        the living being, a predefined body dimension (e.g. height, leg        length or inside leg length), a gender or a category (e.g. human        being/animal/child/cyclist).    -   An ability to move by means of one or several sideways steps.    -   An ability to move by means of one or several backward steps.    -   An ability to move by moving the centre of gravity and/or by        inclining the body of the living being or the pedestrian, which        can be used to deduce a specific movement behaviour, in        particular if it is analysed in conjunction with motion patterns        that have been determined empirically.

In fact, the unique ability of living beings to rotate about their ownaxis, to step sideways or, at least from a standstill position, to walkabruptly backwards, i.e. to move opposite to the current orientation ofthe body, as well as a limited and varied physiological movement abilityin all directions will lead to results that differ significantly fromthose of conventional trajectory algorithms when predicting a probableposition.

The above parameters can for example be determined by evaluating imageinformation and/or location information.

The term “state of motion” of a living being or of a pedestrian alsoincludes a change in movement of said living being or pedestrian. Inthis context, those parameters which indicate an imminent change inmovement of the living being or pedestrian are of particular importance.

While certain parameters, such as the position, orientation,translational speed and acceleration or the curve radius are alsodetected and taken into account for conventional trajectory algorithms,the present method is different in that the probable position is alwayspredicted taking into account the physiological movement ability and/orpreferred directions which are due to the general behaviour of theliving being, i.e. it is not assumed that the current state of motioncontinues unchanged, but it is taken into account and the prediction islimited to what is physiologically possible and/or will probably happendue to general behaviour.

In another further development, a potential future positioncorresponding to the parameters that have been determined is retrievedfrom a database or a family of characteristics; for this purpose, themeasured parameters are for example compared with the parameters thatare stored in the database or the family of characteristics. Theparameters on which the database or family of characteristics is basedcan for example be determined by means of experiments.

As an alternative, one or several of the parameters are supplied to amodel computer in order to determine the position of the living being,wherein said model computer is based on an abstract movement model forliving beings. The measured parameters are supplied to the modelcomputer, which is able to determine the potential future position usingsaid movement model for living beings. This approach has the advantagethat different classes of living beings can be taken into account in asimplified manner by appropriately scaling individual parameters, sothat they are taken into account more or less intensively. Anotheradvantage is that the potential future position can be determined on thebasis of physical facts and empirical data. In this way, a very highaccuracy of the prediction can be achieved.

According to another further development, the current speed, the currentorientation and the current rotation of the body are used to determine apath of movement in order to determine the potential future position.

In another further development, the maximum acceleration ability of theliving being, which is dependent on his/her speed of movement, is takeninto account for the determination of the potential future position.This is based on the consideration that the acceleration ability of aliving being is not constant, but varies over the speed range covered bysaid living being. The same is true for the deceleration ability of aliving being. It has also been found that the deceleration ability of aliving being exceeds its acceleration ability. This finding canadvantageously be used when determining the potential future position.In addition to a maximum acceleration ability in the current directionof movement, a maximum acceleration ability opposite to the currentdirection of movement and/or orientation of the living being ispreferably predefined.

Therefore, at least one of the parameters below is preferably predefinedfor the living being:

-   -   a maximum speed from which the acceleration ability in the        current direction of movement is zero, i.e. the absolute maximum        speed,    -   a maximum acceleration in the direction of orientation of a        non-moving living being as well as opposite to said orientation,    -   a speed at which the maximum acceleration ability in the current        direction of movement is highest,    -   a speed at which the maximum acceleration ability opposite to        the current direction of movement and/or orientation of the        living being is highest in value, i.e. at which the living being        is able to slow down fastest,    -   a maximum speed opposite to the orientation of the living being        from which the acceleration ability opposite to said orientation        is zero. In conjunction with a current form of movement, these        values can then be used to determine the relevant acceleration        ability in the direction of movement and in the opposite        direction, i.e. the ability to slow down. As an alternative,        relevant characteristic curves can of course be stored.

These values are preferably predefined as a function of the class ofliving being concerned, in particular varying according to age, genderand body dimensions.

In another further development, a minimum possible curve radius, whichis dependent on the current walking speed and/or acceleration, is takeninto account for the determination of the potential future position.Knowledge of a minimum possible curve radius makes it possible topredict how fast a living being can change his/her direction, forexample to cross a road or to cross the path of movement of the vehicle.

According to another further development, a maximum decelerationability, which is dependent on the speed of movement and/or a curveradius of the movement made by the living being, is taken into accountfor the determination of the potential future position. This informationcan for example be used to take into account whether a living being thatmay potentially collide with the vehicle is able to stop early enoughbefore reaching a collision zone or to move away from said collisionzone.

According to another further development, an angle at which the livingbeing is positioned or moves relative to a path of travel of the vehicleis taken into account for the determination of the potential futureposition, wherein said angle is used to determine the amount of time ittakes the living being to turn towards the path of travel whileaccelerating substantially at the same time in order to reach the travelpath area. Knowledge of said angle as well as of the amount of timerequired by the living being, for example to reach the road, enable amore precise estimate of a potential future position and hence animproved assessment of the risk of a collision.

The angle taken into account is an angle ranging between 150° and 210°,corresponding to a living being that is positioned or moves with his/herback to the path of travel. As an alternative, the angle taken intoaccount in particular ranges between 60° and 120°, corresponding to aliving being that is positioned or moves with his/her side to the pathof travel. Said path of travel may coincide with the course of a road inthis case.

The potential future position is determined taking into account arelative position of the living being to the path of travel, inparticular a distance at which the living being is positioned or movesrelative to said path of travel, wherein said relative position is usedto determine the amount of time it takes the living being to acceleratein order to reach the travel path area.

Furthermore, it is intended that surroundings information and/orobstacles be taken into account for the determination of the potentialfuture position. This information can for example be obtained by meansof digital maps or by the surroundings sensing system. The accuracy ofprediction of the potential future position can be further increased ifobstacles, e.g. a course of the road, the presence of house walls andthe like, are taken into account.

The position of the living being thus determined serves as an inputvariable for the computation of the trajectory of a trajectory pairwhich is to be suitable for the computation of the probability of acollision.

In another further development, the position is divided into severalsub-positions having different probabilities. In other words this meansthat probabilities are specified for individual sub-positions of apotential future position that has been determined, wherein“probability” means the probability that the living being will belocated at said sub-position within the next milliseconds or seconds, inaccordance with the position measured over time (movement).

Said probabilities can be used to determine the progressive partialtrajectories of a pedestrian included in a trajectory pair, which arerequired to compute the probability of a collision.

The invention also relates to a vehicle comprising a protection systemfor living beings, preferably for pedestrians outside said vehicle, inparticular pedestrian protection devices which, in order to implementthe method, are equipped

-   -   with at least one sensing system to obtain surroundings        information,    -   with a computing unit which evaluates said surroundings        information in order to identify a living being, in particular a        pedestrian, determines movement trajectories for the living        being and the vehicle as a trajectory pair, and uses said        trajectory pair to deduce the probability a of collision and        hence the necessity to activate a protection system, wherein    -   in particular, the sensing system is designed to detect        parameters of living beings and of their physiological movement        ability, and    -   the computing unit is designed to determine the potential future        position at a given moment in time, based on a location of the        movement trajectory and on the state of motion and taking into        account a physiological movement ability of the living being at        one or several future moments in time.

The probabilities of a collision for collision situations between thepedestrian and the vehicle can advantageously be computed by means ofthe computing method described below.

The method according to the invention preferably comprises the followingmethod steps:

-   1. During the initial phase, before the vehicle is put into    operation, a finite number of typical initial situations of motion    (initial state of motion) for different types of pedestrians are    measured and stored in a memory which is located aboard the vehicle.    This initial situation can be defined as follows:    -   Initial situation 1: pedestrian does not move, speed: v=0 m/s,        acceleration a=0 m/s², rate of rotation: w=0°/s;    -   Initial situation 2: an adult pedestrian walks at a speed of v=1        m/s, acceleration a=0 m/s², rate of rotation w=0°/s;    -   Initial situation 2: an adult pedestrian walks at a speed of v=1        m/s, acceleration a=0 m/s² while rotating about his/her vertical        axis at a rate of rotation w=1°/s; . . . .-   2. For each of the initial situations of step 1, a group of    potential movement trajectories for a predefined period of time of    e.g. 3 s comprising increments Δt of e.g. 0.1 s is computed. For    this purpose, the computation method of stochastically modelling the    pedestrian is used. A group of trajectories including the    intermediate position points of the pedestrian is obtained as a    result of these numerical computations for each initial situation of    motion.-   3. The initial situations of motion and the trajectory groups that    have been computed are stored in the memory aboard the vehicle.-   4. Next, the risk of a collision during operation of the vehicle is    computed as follows:    -   4.1. The state of motion of the pedestrian is detected by means        of a suitable sensor system. In addition, the vehicle's own        dynamics are detected at the same moment in time.    -   4.2. The nearest initial situation of motion of the pedestrian,        which was measured and stored in the memory during the initial        phase in step 1, is selected.    -   4.3. The trajectory group which was computed for the selected        initial situation of motion in step 2 of the initial phase and        stored with reference to said initial situation of motion is        retrieved and placed around the position of the pedestrian that        has been detected, in accordance with the orientation of said        pedestrian.    -   4.4. The information obtained in 4.1 to 4.3 is used to compute        the risk of a collision as follows:        -   a. The travel of the vehicle is extrapolated at small time            increments. Said time increments correspond to the time            increments used for the computation of the trajectory group            of the pedestrian: Δt of e.g. 0.1 s. In this way a driving            path is obtained, wherein said driving path comprises areas            for each of said time increments. These areas are areas            where a collision of a pedestrian with the vehicle cannot be            avoided. Said areas will hereinafter be referred to as            collision zone.        -   b. At each time increment Δt, only the position points of            the trajectories of the trajectory group selected in step            4.3 are analyzed, wherein said position points at a            particular time increment reflect the potential positions of            the pedestrian at the time increment concerned. Next, it            will be checked if any or how many of the selected position            points are located within the collision zone of the vehicle.            If this is the case, there will be a single collision            between the vehicle and the pedestrian. The number of            trajectories contained in the trajectory group including the            position points which predict a single collision is            determined.        -   c. Those trajectories where collisions have occurred are            disregarded in the subsequent computation steps for the next            time increments.        -   d. Steps b and c are repeated at time increments Δt in order            to determine the number of single collisions for the            subsequent time increments.        -   e. Steps a to d are continued to be carried out until the            vehicle has passed the pedestrian to an extent that no            further collisions may occur.    -   4.5. In this way, the number of trajectories including at least        one position point which is located in any of the collision        zones of the vehicle is determined, and the quotient of the        number of collision trajectories and the total number of        trajectories is computed. This quotient is an indicator of the        probability of a collision. Said quotient can therefore be used        to determine the risk of a collision.    -   4.6. As an option, the aforesaid quotient is compared with a        number of predefined thresholds. If the quotient is below a        first, lowest threshold, there is no risk of a collision. If the        quotient exceeds the first threshold, but is below a second,        second-lowest threshold, there is a small risk of a collision.        This small risk of a collision can e.g. be eliminated by means        of an alarm signal to the driver of the vehicle. If, however,        the quotient exceeds a last, highest threshold, there is an        imminent risk of a collision between the vehicle and the        pedestrian. In this case, measures to reduce the consequences of        the accident, e.g. autonomous full braking of the vehicle, are        required.-   5. The determination of the probability of a collision according to    step 4 can be repeated iteratively at defined time intervals, e.g.    of 0.5 s. In addition, the travel of the vehicle can also be varied    in another computation loop using a stochastic model.

In more detail, during the initial phase, before the vehicle is put intooperation, a finite number of typical initial situations of motionPx-BS1(v1, a1, w1), Px-BS2(v2, a2, w2), Px-BSn(vn, an, wn) for a modelpedestrian Px is predefined, taking into account the movement ability ofsaid pedestrian. Here, v1, v2, . . . , vn are different initial speeds,a1, a2, . . . , an are different initial accelerations, and w1, w2, . .. , wn are different initial rates of rotation of the model pedestrianPx.

A group of potential movement trajectories BT-Px-BS1, BT-Px-BS2, . . . ,BT-Px-BSn is computed for each of these initial situations of motionPx-BS1(v1, a1, w1), Px-BS2(v2, a2, w2), . . . , Px-BSn(vn, an, wn) for apredefined period of time (e.g. 3 s) comprising increments Δt (e.g. of0.1 s). The computation method used includes stochastic modelling of thepedestrian. A group of trajectories including the intermediate positionpoints of the model pedestrian Px is obtained as a result of thesenumerical computations for each initial situation of motion. Said modelpedestrian Px can for example represent 90% of all adult men.

Further initial situations of motion are defined for other groups ofpedestrians, such as adult women, elderly pedestrians, children, as wellas for cyclists or animals such as dogs, and relevant groups of movementtrajectories are determined.

Said initial situations of motion and the associated trajectory groupsthat have been determined are stored in an internal memory of thevehicle for later use.

During operation of the vehicle or while driving through a city centre,first the pedestrians in the proximity of the vehicle, in particular inthe area of or near the driving path of the vehicle, are detected bymeans of the surroundings sensing system which is located aboard thevehicle.

In addition, the states of motion of the detected pedestrians aredetected by means of suitable sensors, e.g. in the form of speed,acceleration and rate of rotation values v0, a0, w0, . . . . Thesestates of motion are used as initial situations of motion for thedetermination of the risk of a collision. The states of motion vx, ax,wx of pedestrians detected earlier are preferably continued to bedetected.

At the same time, the vehicle's own dynamics, i.e. its speed,acceleration and/or rate of rotation, are detected. The travel of thevehicle is extrapolated at small time increments, based on the measuredvalues relating to the vehicle's own dynamics. Said time incrementscorrespond to those used to compute the trajectory group of thepedestrian during the initial phase, i.e. Lt. In this way, a drivingpath is obtained, wherein said driving path comprises areas for eachtime increment. These areas are the collision zones at each of said timeincrements.

If a pedestrian P0 is detected, the state of motion values v0, a0, w0, .. . of said pedestrian P0 are compared with the typical initialsituation of motion values Px-BS1(v1, a1, w1), Px-BS2(v2, a2, w2), . . .Px-BSi(vi, ai, wi), . . . , PxBSn(vn, an, wn) which were measured andstored during the initial phase.

As an option, the type of the pedestrian P0 is determined before thestate of motion values are compared, i.e. the data measured for thispedestrian P0 by means of the surroundings sensing system is used todecide which group of pedestrians said pedestrian P0 should belong to.If the data measured by the surroundings sensing system comprisescharacteristic features of an adult male pedestrian, the newly detectedpedestrian P0 is categorized as belonging to the group of “adult men”.If, however, the data measured by the surroundings sensing systemcomprise characteristic features of a child, the pedestrian P0 iscategorized as belonging to the group of “children”. This allocation toa group facilitates the retrieval of the relevant initial situation ofmotion values from the memory from among the numerous initial situationof motion values which were measured and stored during the initialphase.

If the newly detected pedestrian P0 is categorized as belonging to thegroup of “adult men”, only those initial situation of motion valuesPx-BS1(v1, a1, w1), Px-BS2(v2, a2, w2), . . . , Px-BSi(vi, ai, wi), . .. , Px-BSn(vn, an, wn) which were stored with reference to the group of“adult men” are retrieved and used for a comparison with the state ofmotion values v0, a0, w0.

If the state of motion values v0, a0, w0, . . . of the pedestrian P0 aremost similar to a set of initial situation of motion values, e.g.Px-BSi(vi, ai, wi), the group of movement trajectories BT-Px-BSi whichwas stored with reference to this set of initial situation of motionvalues Px-BSi(vi, ai, wi) is used to determine a collision.

The selected group of movement trajectories BT-Px-BSi belonging to theaforesaid initial situation of motion values PxBSi(vi, ai, wi) is placedaround the detected position of the pedestrian P0 in a suitableorientation, wherein said orientation is preferably the orientation ofthe pedestrian P0 relative to the direction of magnetic north andwherein the starting point of the group of movement trajectoriespreferably overlaps the centre point of said pedestrian P0.

The position points of the trajectories of the selected trajectory groupare used to determine the risk of a collision at each of the aforesaidtime increments Δt, wherein said position points at each time incrementreflect the potential positions of the pedestrian at the time incrementconcerned.

Next, it will be checked how many of these selected position points arelocated within the relevant collision zone of the vehicle. Each of theposition points located within the collision zone indicates a singlecollision between the vehicle and the pedestrian. The number oftrajectories contained in the trajectory group including the positionpoints which predict a single collision is determined. The trajectoriesincluding said collision position points are disregarded in thesubsequent computation steps for the following time increments.

The position points which are located within the collision zone and thenumber of trajectories including these position points are continued tobe determined at time increments of Δt until the vehicle has passed thepedestrian to an extent that no further collisions may occur.

Subsequently, the number of all (collision) trajectories where at leastone position point is located within the collision zones is determined,and the quotient of the number of collision trajectories and the totalnumber of trajectories is computed. This quotient indicates theprobability of a collision. Said quotient can therefore be used todetermine the risk of a collision.

Advantageously, the aforesaid quotient is compared with a number ofpredefined thresholds. If the quotient is below a first, lowestthreshold, there is no risk of a collision. If the quotient exceeds thefirst threshold, but is still below a second, second-lowest threshold,there is a small risk of a collision. This small risk of a collision cane.g. be eliminated by means of an alarm signal to the driver of thevehicle. If, however, the quotient exceeds a last, highest threshold,there is an imminent risk of a collision between the vehicle and thepedestrian. In this case, measures to reduce the consequences of theaccident, e.g. autonomous full braking of the vehicle, are required.

The method for computing the risk of a collision described aboverequires much less computing time and enables the probability of acollision to be computed almost in real time.

By means of the computation method described above, the risk of acollision can be computed in the required real time when a collisionsituation arises.

The invention will now be explained with reference to the drawings, inwhich:

FIG. 1 shows a schematic view of a scene including a vehicle and apedestrian, which is intended to explain the method according to theinvention,

FIG. 2 shows a diagram which illustrates the interrelationship betweenthe lateral acceleration and deceleration abilities of a living being asa function of a speed reached by said living being,

FIG. 3 shows a diagram which illustrates the interrelationship betweenthe rotation ability of a living being as a function of a lateral speedreached by said living being,

FIG. 4 shows a polar diagram which illustrates the range of motion of ahuman being from a standstill position, taking into account the lateralacceleration ability and the rotation ability,

FIG. 5 shows a polar diagram which illustrates the range of motion of ahuman being from a standstill position, taking into account the lateralacceleration ability, the rotation ability as well as the ability tomove sideways and backward,

FIG. 6 shows a diagram which illustrates the range of motion in thelongitudinal and transverse directions of a human being that moves at acertain speed,

FIG. 7 shows a flow chart which illustrates the method for determiningthe trajectory of a pedestrian,

FIG. 8 shows a schematic view which illustrates the determination oftrajectory groups for a finite number of typical initial situations ofmotion for different types of pedestrians during the initial phase, and

FIG. 9 shows a schematic view which illustrates the determination of theprobability of a collision according to the invention.

To determine the probability of a collision between a vehicle and aliving being, in particular a pedestrian, cyclist or animal, a reliableprediction of the path of movement of a vehicle (so-called driving path)on the one hand and of the path of movement (so-called trajectory) ofthe living being on the other is required. While the driving path of avehicle can already be determined with high precision on the basis of akinematic model, the determination of the path of movement of the livingbeing is subject to a plurality of elements of uncertainty which must betaken into account in a behavioural model describing the behaviour inspace and time.

FIG. 1 schematically shows a scene including a vehicle 1 and apedestrian 2, wherein the vehicle 1 moves in the direction of the arrow5.

The method according to the invention for computing the probability of acollision starts from the current positions and states of motion of thevehicle 1 and the pedestrian 2 at a moment in time T₀.

These positions are used to determine the further paths of movement forthe vehicle 1, using a kinematic model, and for the pedestrian 2, usinga behavioural model, on the basis of time increments Δt's, wherein eachΔt is a prediction period. In this way, progressive trajectories overthe subsequent prediction periods Δt's can simultaneously be determinedfor the vehicle 1 and for the pedestrian 2 as a trajectory pair, whereineach trajectory is composed of partial trajectories which have beendetermined for the prediction period Δt. Since various movement optionswill be obtained for the pedestrian for each prediction period Δt, whichas a rule is only true to a limited extent for the vehicle 2, severaltrajectory pairs for the moment in time T₀ are determined by means ofthe method according to the invention.

The trajectory or driving path 3 of the vehicle 1 can be predicted quiteprecisely and reliably for several subsequent prediction periods Δt onthe basis of the kinematic data that has been detected, such as speed,acceleration and direction. The relatively simple kinematic model can ofcourse be complemented by a driver behaviour model.

On the basis of the behavioural model that is applied, the currentposition and the current state of motion of the pedestrian 2 are used todetermine his/her first partial trajectory belonging to the firstprediction period Δt, whereas the further incremental sequence of motionfor the subsequent prediction periods Δt's is “guessed” by means of arandom generator, wherein, however, only those movements that areallowed by the behavioural model are analyzed and a probabilitydistribution on which the behavioural model is based is taken intoaccount. For this purpose, sequences of motion or behavioural models ofpedestrians can for example be taken into account by limiting thefrequency distributions in a targeted manner when determining thefurther sequence of motion by means of a random generator.

The aforesaid method for computing the progressive trajectories iscontinued until the two trajectories of a trajectory pair would collideor cannot collide any more. For this purpose, it is assumed that therewould be a collision if the pedestrian 2 has come so near to the vehicle1 that a predefined minimum distance is no longer maintained during therelative motion of the two road users.

The probability of a collision is computed as a collision risk valueobtained from the number of trajectory pairs which would indicate acollision and the total number of trajectory pairs that have beencomputed for the moment in time T₀. According to FIG. 1, 7 trajectorypairs were determined starting from a fixed moment in time T₀, whereinonly one trajectory is shown as potential path of movement of thevehicle 1 for the sake of simplicity. At a moment in time T₀+Δt+ . . .+Δt+ . . . at which the vehicle has passed the pedestrian completely,five of said 7 trajectory pairs indicate a collision; therefore thecollision risk value determined by computation is 5/7.

This collision risk value is initially valid for a predefined initialstate according to FIG. 1 at the moment in time T₀. To determine theprobability of a collision during the course of the scene according toFIG. 1 following the moment in time T₀, the computation explained aboveis repeated at time increments T₁, T₂, T₃ . . . , starting from thecurrent positions and the current states of motion of the vehicle 1 andthe pedestrian 2 in each case. In this way, a large number of potentialfuture paths of movement in the form of a group of trajectory pairs areobtained for each of these moments in time T₁, T₂, T₃ . . . , whichtrajectory pairs always start from the current, actual trafficsituation. Said group of trajectory pairs will then be the basis for thecomputation of the collision risk value for each of these moments intime T₁, T₂, T₃ . . . , and a chronological development of the collisionrisk values representing the probability of a collision will be obtainedas a result.

This method according to the invention for determining the probabilityof a collision is a realistic and mathematically sound method, wherein amuch broader prediction horizon is achieved, i.e. a long-term, yetreliable prediction is made.

This is in addition also achieved by the fact that the movement abilityof the collision parties is taken into account when determining theprobability of a collision, in particular the limited physiologicalmovement ability of a living being, in particular a pedestrian. Thebehavioural model of a pedestrian thus takes into account both thephysical movement options and the physiological movement ability.

In particular, the typical motion patterns or features indicating suchtypical motion patterns of a pedestrian are taken into account which canbe characterized as indicators and can therefore be sensed in order todetermine potential positions and finally the potential future position.

When analyzing the physiological movement ability, highly diverse statesof motion as well as combinations of potential states of motion aretaken into account.

For example, the maximum acceleration from a standstill position istaken into account without rotation, with a rotation over 90° and with arotation over 180°. When considering the maximum acceleration ability ofa pedestrian from a standstill position, it was found that saidacceleration ability first increases from an initial value to a maximumvalue and then decreases more or less constantly as the speed of thepedestrian increases. For a rotation over 180°, it was found that themaximum acceleration ability is highly dependent on age on the one handand differs widely, both up and down, around a statistical average.Compared to the acceleration ability from a standstill position,however, only small acceleration values can be reached here.

In an analogous manner, the maximum deceleration ability of a pedestrianwalking at full speed is taken into account, both without a turn andwith a maximum change in direction. Strong age-dependent differenceswere found here as well. The deceleration ability of a pedestrianwalking at full speed without a change in direction exceeds the maximumacceleration ability of said pedestrian.

Another parameter that affects the potential position is the maximumacceleration when walking at a certain speed. The following typicalcases are taken into account here: a 90° turn to the left and right anda 45° turn to the left and right. In this context, minimum possiblecurve radii of the pedestrian were determined. It was found that allpedestrians, irrespective of their age, were not able to move at aradius below a minimum curve radius. This information is valuable inorder to estimate at which position a pedestrian can turn and movetowards a road where a vehicle is approaching, and, if applicable, howmuch time it takes him/her to do so.

In an analogous manner, curve radii to the left and right weredetermined for a pedestrian walking at full speed.

To assess the physiological movement ability, a forward jump and a jumpto the side were also taken into account. The times and distances thatcan be reached here can suitably be used to determine the ability, inparticular of a pedestrian, to react in a sudden emergency.

FIG. 2 shows a diagram illustrating the acceleration and decelerationability of a pedestrian as a function of his/her walking speed.

The term “current direction of movement/orientation” means that thepedestrian is assumed to move in accordance with the orientation ofhis/her body, i.e. in particular his/her trunk, wherein a non-movingpedestrian has no direction of movement, but certainly a particularorientation.

The positive acceleration ability in the current direction ofmovement/orientation is shown in quadrant Q1. Quadrant Q2 shows thenegative acceleration ability, i.e. the ability to slow down, duringforward movement, whereas quadrants Q3 and Q4 refer to a movementopposite to the orientation: Q3 describes the negative accelerationability for this direction of movement, i.e. slowing down and, ifapplicable, accelerating in the normal direction again, while Q4 showsthe acceleration ability during backward movement.

Referring to FIG. 2, the first decisive difference from conventionaltrajectory algorithms to be stated is that a defined accelerationability, both in the direction of orientation and in the oppositedirection, is specified even for a non-moving pedestrian.

As can be clearly seen in the diagram, the maximum acceleration abilitya_(max) and the maximum deceleration ability −a_(max) do not correspondto an approximately equal speed v, but the acceleration ability startsto decrease early as the speed increases, whereas a considerably higherdeceleration ability can be found even at higher speeds. Thedeceleration ability of a pedestrian exceeds in value his/heracceleration ability.

In addition, an acceleration ability opposite to the orientation istaken into account for the first time, although vehicles are also ableto travel backwards, but this can still be taken into account in thetrajectory, if applicable. If, however, the physiological movementability is taken into account appropriately, FIG. 2 shows that theacceleration ability as well as the maximum speed opposite to theorientation clearly differ from those during normal forward movement.

If for example the following parameters for the pedestrian are specifiedfor an algorithm:

a maximum speed from which the acceleration ability in the currentdirection of movement is zero,

a maximum acceleration in the direction of orientation of a non-movingpedestrian as well as opposite to said orientation,

a speed at which the maximum acceleration ability in the currentdirection of movement is highest,

a speed at which the maximum acceleration ability opposite to thecurrent direction of movement and/or orientation of the pedestrian ishighest in value,

a maximum speed opposite to the orientation of the pedestrian from whichthe acceleration ability opposite to said orientation is zero,

these parameters can be used to deduce the acceleration and decelerationabilities in each case in a relatively simple manner and with sufficientprecision.

The aforesaid parameters are preferably predefined as a function of theclass of pedestrian, in particular varying according to the age, gender,and body dimensions since there are significant differences here.

If just this interrelationship between the acceleration and speed of apedestrian is taken into account, the potential position can bepredicted and hence the probability of a collision can be determinedmuch more precisely, compared to the state of the art.

In an analogous manner, FIG. 3 shows the ability to rotate about the ownaxis, wherein said rotation ability is normally symmetrical, but isclearly higher in the forward direction than during backward movement,while a decreasing though quite surprising rotation ability ismaintained even at high speeds. Therefore, this parameter of thephysiological movement ability also differs decisively from classicaltrajectory algorithms since these do not include a rotation about theown axis, let alone from a standstill position.

The physiological movement ability to the side, i.e. transversely to theorientation of the body and the normal walking direction, is in additionaffected by the ability to step sideways. This ability to step sidewaysis significant in a standstill position and, even at a low speed ofmovement, results in the differences with regard to the maximumreachable area, a comparison of which is shown in the following FIGS. 3and 4, but clearly decreases as the walking speed increases and can beomitted for normal forward movement if required and replaced with anincreased rotation ability.

FIG. 4 shows a polar diagram which illustrates the range of motion of anon-moving pedestrian, taking into account his/her lateral androtational acceleration ability and disregarding sideways and backwardmovements. The polar diagram covers angles ranging from 0° to 360°. Anangle of 0° means that the pedestrian walks straight on. The polardiagram further includes concentric circles which are marked with 0.5,1, 1.5, and 2. These are the distances (e.g. in metres) relative to thecentre point where the human being is located at the moment in time t₀.At the moments in time t₁, t₂, t₃, t₄, t₅, the human being can belocated within the ISO lines corresponding to said moments in time,wherein t₅>t₄>t₃>t₂>t₁.

Due to the physiological movement ability of the human being, he/she canmove at a moment in time t₁ in an area which is enclosed by thecorresponding ISO line. Essentially, a forward movement (i.e. in thewalking direction, angle=0°) is possible here, while it is hardlypossible to deviate from said 0° angle to the left (counter-clockwise)or to the right (clockwise). At a moment in time t₂ (t₂>t₁), the areawidens in the forward direction as well as to the right and left (cf.the ISO line indicated by t₂). In an analogous manner, at a moment intime t₅ (t₅>t₄>t₃>t₂>t₁), the pedestrian can be located in the areaenclosed by the corresponding ISO line. Here, not only a forwardmovement, but also a movement sideways towards the back is possible.

It will be apparent from the polar diagram that the physiologicalmovement ability at the moments in time t₁ to t₅, which are in thefuture compared to t₀, will not allow movement in the angle rangebetween 120° and 240°. This finding is important, e.g. if the pedestrianis positioned with his/her back to the road. The physiological range ofmotion only allows the pedestrian to move straight on (angle=0°),wherein short-term deviations are only possible in an angle range ofless than ±90° and deviations of ±120° are only possible at a latermoment in time (moment in time t₅). It can also be seen here that thedistance which can be covered by the pedestrian becomes smaller as theangle increases. The illustration does not take into account that apedestrian can also step backwards (angle=180°), but the distance whichcan be covered in this case is small.

If these sideways and backward forms of movement are included, onceagain a clear change in the movement area is obtained, as shown in FIG.5. The result is an approximately elliptical pattern, wherein the centreof gravity of the ellipse is clearly displaced from the zero point inthe direction of the normal orientation since the movement ability inthe direction of orientation is higher than opposite to saidorientation.

FIG. 6 shows a diagram illustrating the range of motion in thelongitudinal direction s_(l) and in the transverse direction s_(q) of ahuman being who moves at a speed v. It is assumed that the pedestrian isat the origin of the coordinates at a moment in time 0 and moves at apredefined speed in the longitudinal direction (i.e. along the x axis).At a moment in time t=0.4 s and taking into account all parameters, thepedestrian can be located in the hatched movement area marked with BAB1.At a moment in time t=0.6 s, the pedestrian can be located in the areamarked with BAB2. In an analogous manner, the potential movement areasBAB3 at the moment in time t=0.8 s and BAB4 at the moment in time t=1 sare shown. It is apparent that the movement area widens, i.e. extends inthe transverse direction s_(q), on the one hand and has a greater depthon the other as time progresses. This is due to the fact that thepotential options of the pedestrian in respect of his/her movementbecome more varied as time progresses, so that the potential movementarea will also increase in size as a consequence.

FIG. 6 only shows movement areas BAB1, . . . , BAB4 in one transversedirection (to the left in the present exemplary embodiment). Of course,the movement area also extends in the other transverse direction, andthe diagram shown in FIG. 6 must therefore be mirrored about the x axis.

FIG. 7 shows a flow chart which illustrates the method for determiningthe trajectory of a pedestrian. In a step S1, an ACTUAL position of apedestrian is detected. This can e.g. be done by means of picturerecording means in a vehicle. In a step S2, adverse effects on theposition information (ST) are taken into account, which may e.g. becaused by measurement errors and the like. The clean data that has beendetermined in step S2 is used to determine a chronology, i.e. a historyof movement of the pedestrian, in a step S3. It is e.g. sufficient ifsaid history goes back 0.5 to 1 s into the past. This information servesto determine a movement trajectory on the one hand and a state of motionof the pedestrian on the other. The current state of motion of thepedestrian is determined in a step S5. In a step S6, the physical rangeof motion of the pedestrian is determined, taking into account thephysiological movement ability of said pedestrian. This range of motioncorresponds to the potential future movement area where the pedestriancan be located due to his/her orientation, walking speed, translationaland/or rotational movement, his/her curve radius, his/her age, theground friction coefficient, etc. Finally, a probability distribution ofthe range of motion or the movement area is determined in a step S7.Here, the movement area is divided into a number of different areas eachhaving a probability that the pedestrian will be located there. Theresult is supplied to an evaluation unit AE. The current path ofmovement of the pedestrian, i.e. his/her movement trajectory, isdetermined in a step S6, which can be carried out parallel to step S5.The future path of movement of the pedestrian is determined in a stepS7, taking into account restrictions caused by the surroundingconditions, and supplied to the evaluation unit AE. In parallel, typicalmotion patterns can be taken into account in a step S8. These may e.g.include findings as to how a pedestrian behaves at a traffic light orzebra crossing. This information is used in the attempt to determine anexpected preferred direction of movement. Said information is alsosupplied to the evaluation unit AE, which uses the information suppliedto determine a movement horizon of the pedestrian in a step S10. Saidmovement horizon once again corresponds to the movement area or theposition.

This method enables a much more precise prediction of the probability ofa certain position of a pedestrian or cyclist or an animal in the nearfuture, based on a position measured over time.

This method and the method for determining the probability of acollision are for example jointly implemented in a control device, whichuses the movement options of the vehicle and of the living being tocompute a collision risk value indicating the probability of acollision, wherein the prediction quality is increased by taking intoaccount the physiological movement ability of the living being.

As is apparent from the above description, a human being can deceleratemuch faster than accelerate, or cannot change direction or only makedirectional changes with small radii at higher walking speeds. Thismovement ability in addition differs according to individualcircumstances, such as age, gender, fitness, etc, and is e.g. determinedby means of tests before being implemented in an algorithm. Theinformation can e.g. be stored in a memory and retrieved and used inaccordance with the input data that has been determined in each case fora more precise determination of the probability of a certain position.

Moreover, characteristic motion patterns of living beings, in particularin typical traffic situations (e.g. at zebra crossings, traffic lights,etc.), can be determined by means of tests or traffic monitoring andtaken into account in the method. Said motion patterns are compared withthe movement of the living being that has been measured or determined,thus also increasing the prediction accuracy.

In addition, surroundings information can be taken into account, whichcan be supplied by navigation systems or digital maps. Moreover, acombination with state observers (a combination of digital maps with asurroundings sensing system) is possible. Restrictions of the movementoptions caused by obstacles (e.g. in the course of a road, house wallsand the like) can be taken into account, thus also increasing theprediction accuracy. This can also be taken into account when predictingthe future position of the vehicle.

To carry out these methods, a vehicle can be equipped with a suitablesensing system for detecting parameters of living beings or pedestrians,in particular those parameters defining their physiological movementability, wherein a computing unit is designed to determine the potentialfuture position or the progressive trajectory pairs at a given moment intime, based on a location of the movement trajectory and the state ofmotion and taking into account the physiological movement ability of theliving being at one or several future moments in time. In particular todetermine the trajectories of a pedestrian, relevant families ofcharacteristics and physiological models can for example be stored, andthe computing unit can then determine the probable position using theaforesaid parameters. In this way, a protection system for living beingsor pedestrians outside the vehicle, in particular pedestrian protectiondevices, can be activated much more precisely, and false alarms can bemuch reduced.

Trajectory groups for a finite number of typical initial situations ofmotion for different types of pedestrians are predetermined and storedin the memory located aboard the vehicle during the initial phase,before the vehicle is put into operation, as illustrated in FIG. 8.

The trajectory groups are preferably determined once for each type ofpedestrian for all potential initial situations of motion, and arestored with reference to the type of pedestrian concerned and thespecific initial situation of motion.

A trajectory group for the pedestrians 100 of the group of “adult men”and the initial situation of motion BSi (vi, ai, wi) is determined asexplained below. In the explanation, vi, ai and wi mean the initialspeed, the initial acceleration and the initial rate of rotationrespectively of the adult model man 100.

Based on this initial situation of motion BSi (vi, ai, wi), allpotential typical movement trajectories ti1, . . . ti10 of the modelpedestrian 100 are determined for a preferred period of time of 3 s attime increments of Δt=0.1 s. To provide a simplified illustration of themethod according to the invention, the trajectory group is symbolized byjust 10 trajectories in FIG. 8.

At the first measurement time t1, wherein t1=Δt=0.1 s, e.g. 10 positionpoints or positions p10, . . . , p19 and the corresponding trajectoriesTi1, . . . , Ti9 are determined. Since the pedestrian 100 is able toabruptly change his walking direction and walk in any direction, asdiscussed in the above description, the position points are in partlocated behind said pedestrian 100, i.e. in the direction opposite tothe current orientation of the pedestrian 100 in the initial situationof motion (direction of the arrow). The position points p10, . . . , p19jointly form a circle pk1, which will hereinafter be referred to asposition circle. In fact, each point within this position circle is apotential position point of the pedestrian 100 at the moment in time t1.Since the pedestrian 100 has certain dimensions and a certain shape,such as e.g. width, depth, those position points that are close to eachother are grouped and shown by just a few position points p10, . . . ,p19, as illustrated in FIG. 8. The trajectories Ti1, . . . , Ti10belonging to these position points p10, . . . , p19 jointly form atrajectory group for this type of pedestrians 100 and for their initialsituation of motion BSi(vi, ai, wi). The number of trajectories in thistrajectory group is 10.

Further position points p20, . . . , p29; p30, . . . , p39; p40, . . . ,p49 for the trajectories Ti1, . . . , Ti10 that have already beendetected are determined at subsequent moments in time t2=2*Δt=0.2 s, t3,t4.

To provide a simplified illustration of the method according to theinvention, the trajectories are only taken into account for a period oftime of 0.4 s here. Depending on the implementation, however, a periodof time of approx. 3 s or more is taken into account.

The trajectory group TSi thus determined, which comprises 10trajectories Ti1, . . . , Ti10 including the position points p10, . . ., p19; p20, . . . , p29; p30, . . . , p39; p40, . . . , p49, and theparameters of the initial situation of motion BSi(vi, ai, wi) are storedin a memory aboard the vehicle 200 with reference to the pedestriangroup of “adult men” for later use.

Further initial situations of motion for other groups of pedestrians,such as adult women, elderly pedestrians, children, cyclists or animalssuch as dogs are defined, and relevant groups of movement trajectoriesare determined and stored aboard the vehicle 200.

During operation of the vehicle 200 or while driving through a citycentre, first the pedestrians 100 in the proximity of the vehicle, inparticular in the area or near the driving path 210 of the vehicle 200,are detected by means of the surroundings sensing system located aboardthe vehicle 200, as illustrated in FIG. 9.

In addition, the states of motion of the detected pedestrians 100 aredetected by means of suitable sensors, e.g. in the form of speed,acceleration and rate of rotation values v0, a0, w0. These states ofmotion are used as initial situations of motion BS0(v0, a0, w0) for thedetermination of the risk of a collision between the vehicle 200 and thepedestrian 100.

At the same time, the vehicle's 200 own dynamics, i.e. its speed,acceleration and/or rate of rotation, are detected. The travel of thevehicle is extrapolated at small time increments, based on the measuredvalues relating to the vehicle's 200 own dynamics, thus obtaining adriving path 210, wherein said driving path comprises areas 221, 222,223, 224 at respective time increments Δt or moments in time t1, t2, t3,t4. These areas 221, 222, 223, 224 are the collision zones at the timeincrements concerned. The time increments Δt correspond to the timeincrements used for computing the trajectory group of the pedestrianduring the initial phase in FIG. 8.

If a pedestrian 100 is detected on the edge of the driving path 210, thestate of motion values v0, a0, w0 of said pedestrian 100, which weremeasured directly by the surroundings sensing system located aboard thevehicle 200 or were measured by an inertial sensor carried by thepedestrian 100 and transmitted to the vehicle 200, are compared with thetypical initial situation of motion values BS1(v1, a1, w1), Px-BS2(v2,a2, w2), . . . , BSi(vi, ai, wi), . . . , BSn(vn, an, wn) which weremeasured and stored during the initial phase.

Optionally, the type of pedestrian, i.e. the pedestrian group thispedestrian 100 should belong to, is determined using the data measuredby the surroundings sensing system or the inertial sensor for thispedestrian 100 before the state of motion values are compared. If thedata measured by the surroundings sensing system or the inertial sensorhave characteristic features of an adult male pedestrian, the pedestrian100 is categorized as belonging to the group of “adult men”.

If the newly detected pedestrian 100 is categorized as belonging to thegroup of “adult men”, only those initial situation of motion valuesBS1(v1, a1, w1), BS2(v2, a2, w2), . . . , BSi(vi, ai, wi), . . . ,BSn(vn, an, wn) which were stored with reference to said group of “adultmen” are retrieved and used for a comparison with the state of motionvalues v0, a0, w0.

If the state of motion values v0, a0, w0 of the pedestrian 100 are mostsimilar to a set of initial situation of motion values, e.g. BSi(vi, ai,wi), the group of movement trajectories TSi which was stored withreference to this set of initial situation of motion values BSi(vi, ai,wi) is used to determine a collision.

This selected group of movement trajectories TSi belonging to theseinitial situation of motion values BSi(vi, ai, wi) is placed around thedetected position of the pedestrian 100 in a suitable orientation,wherein said orientation preferably corresponds to the orientation ofthe pedestrian 100 relative to the direction of magnetic north (thedirection of the arrow in FIG. 9), wherein the starting point of thegroup of movement trajectories TSi preferably overlaps the centre pointof the pedestrian 100.

The position points p10, . . . , p19; p20, . . . , p29; p30, . . . ,p39; p40, . . . , p49 of the trajectories Ti1, . . . , Ti10 of theselected trajectory group TSi are used to determine the risk of acollision at each of the aforesaid time increments Δt=0.1 s or at eachmoment in time t1, t2, t3, t4 in the analyzed time interval of 0.4 s,wherein the position points at the respective moments in time t1, t2,t3, t4 reflect the potential position points p10, . . . , p19; p20, . .. , p29; p30, . . . , p39; p40, . . . , p49 of the pedestrian 100 at therespective moments in time t1, t2, t3, t4.

Next, it will be checked how many of these position points p10, . . . ,p19; p20, . . . , p29; p30, . . . , p39; p40, . . . , p49 are locatedwithin the relevant collision zone 221, 222, 223, 224 of the vehicle200. Each of the position points which is located within the collisionzone 221, 222, 223, 224 indicates a single collision between the vehicle200 and the pedestrian 100. In FIG. 9, these are the position points p10at the moment in time t1, p21, p29 at the moment in time t2, p23 at themoment in time t3.

The number of trajectories Ti1, Ti2, T3, Ti10 of the trajectory groupincluding the position points which predict a single collision isdetermined. In the present embodiment, this number is 4. Thetrajectories Ti1, Ti2, T3, Ti10 including said collision position pointsare disregarded in the subsequent computation steps for the followingtime increments. For example, the trajectory Ti1 including the positionpoint p10 which is located within the collision zone 221 at the momentin time t1 is disregarded when analyzing the following moments in timet2, t3, t4. Analogously, the trajectories Ti2, Ti10 whose positionpoints p21, p29 are located within the collision zone 222 at the momentin time t2 are disregarded when analyzing the following moments in timet3, t4.

The position points which are located within the collision zone and thenumber of trajectories including these position points are continued tobe determined at time increments of Δt until the vehicle 200 has passedthe pedestrian 100 to an extent that no further collisions may occur.

Subsequently, the number of all (collision) trajectories where at leastone position point is located within the collision zones is determined,and the quotient of the number of collision trajectories and the totalnumber of trajectories is computed. This quotient indicates theprobability of a collision. Said quotient can therefore be used todetermine the risk of a collision.

In the present embodiment according to FIG. 9, this quotient is:

$\begin{matrix}{Q = \frac{{Number}\mspace{14mu} {of}\mspace{14mu} {collision}\mspace{14mu} {trajectories}}{{Number}\mspace{14mu} {of}\mspace{14mu} {trajectories}}} \\{= \frac{4\left( {{{Ti}\; 1},{{Ti}\; 2},{{Ti}\; 3},{{Ti}\; 10}} \right)}{10\left( {{{Ti}\; 1},\ldots \mspace{14mu},{{Ti}\; 10}} \right)}} \\{= 0.4} \\{= {40\%}}\end{matrix}$

Advantageously, the aforesaid quotient is compared with a number ofpredefined thresholds. If the quotient is below a first, lowestthreshold, there is no risk of a collision. If the quotient exceeds thefirst threshold, but is still below a second, second-lowest threshold,there is a small risk of a collision. This small risk of a collision cane.g. be eliminated by means of an alarm signal to the driver of thevehicle. If, however, the quotient exceeds a last, highest threshold,there is an imminent risk of collision between the vehicle and thepedestrian. In this case, measures to reduce the consequences of theaccident, e.g. autonomous full braking of the vehicle, are required.

In the present embodiment, the quotient has a value of 0.4, whichindicates e.g. a relatively high risk of a collision. In this case, thevehicle transmits an acoustic signal to the driver and optionally alsoto the pedestrian, thus alerting the driver and the pedestrian to theimminent risk of a collision.

1. A method for determining the probability of a collision of a vehicle(1) with a living being (2), in which the behaviour in space and time ofthe living being (2) is modelled by means of a behavioural model and thebehaviour in space and time of the vehicle (1) is modelled by means of akinematic model and, starting from the current positions of the vehicle(1) and the living being (2), at least one trajectory (4) for each ofthem is determined, characterized in that b) the current positions ofthe living being (2) and of the vehicle (1) are used to computetrajectories (3, 4) of the vehicle (1), based on the kinematic model,and of the living being (2), based on the behavioural model, as atrajectory pair until said trajectory pair either indicates a collisionor indicates no collision, c) the number of trajectory pairs indicatinga collision is determined, and d) the probability of a collision isdetermined as the quotient of the number of trajectory pairs indicatinga collision and the total number of trajectory pairs that have beencomputed.
 2. A method according to claim 1, characterized in that acollision is indicated if the distance between the vehicle (1) and theliving being (2) which is indicated by the trajectories (3, 4) of atrajectory pair is below a predefined threshold. 3-30. (canceled) 31.The method according to claim 1, characterized in that the method stepsb) to d) are repeated at time increments (T₁, T₂, T₃, . . . ).
 32. Themethod according to claim 1, characterized in that the behavioral modelis used to determine potential positions of the living being (2) for oneor for several moments in time, taking into account the state of motionat the time when the computation of a trajectory pair starts.
 33. Themethod according to claim 1, characterized in that the behavioral modeltakes into account the physical and physiological movement ability ofthe living being (2) and/or behavioral patterns of the living being (2)that have been determined empirically.
 34. The method according to claim33, characterized in that the behavioral model is used to determinepotential positions of the living being (2) for one or for severalmoments in time, taking into account the state of motion at the timewhen the computation of a trajectory pair starts, and in that one orseveral of the following parameters are determined and processed asparameters for the determination of the state of motion and/or of thepotential future position: a rotational speed of the living being (2), arotational acceleration about a vertical axis of the living being (2), acurrent radius of curvature of the movement of the living being (2), achange in a direction of movement or of a radius of curvature of themovement of the living being (2), an inertia of the living being (2), aground friction coefficient of the road surface, which in particulardepends on the weather, a class of the living being (2), in particularan age, a predefined body dimension (e.g. height, leg length or insideleg length), a gender or a category (e.g. humanbeing/animal/child/cyclist), an ability to move by means of one orseveral sideways steps, an ability to move by means of one or severalbackward steps, an ability to move by moving the center of gravity, andan ability to move by inclining the body.
 35. The method according toclaim 34, characterized in that one or several of the followingparameters are determined and processed as parameters for thedetermination of the state of motion and/or of the potential futureposition: a position of the living being (2), an orientation of theliving being (2) relative to the surroundings, a translational speed ofthe living being (2), a translational acceleration of the living being(2), the chronological development of at least one of the aforesaidparameters.
 36. The method according to claim 34, characterized in thata potential future position of the living being (2) which has referenceto the parameter(s) that has/have been determined or to thechronological development of at least one of the parameters that havebeen determined is retrieved or determined from a database or a familyof characteristics or an analytical model.
 37. The method according toclaim 34, characterized in that one or several of the parameters aresupplied to a model computer in order to determine a potential positionof the living being (2), wherein said model computer is based on anabstract motion model for living beings (2).
 38. The method according toclaim 33, characterized in that a path of movement, which is dependenton the current speed, the current orientation and the current rotationof the body, is taken into account for the determination of thepotential future position.
 39. The method according to claim 33,characterized in that the maximum acceleration ability of the livingbeing (2), which is dependent on his/her speed of movement, is takeninto account for the determination of the potential future position. 40.The method according to claim 39, characterized in that dependent on thespeed of movement of the living being (2) and in addition to a maximumacceleration ability in the current direction of movement, a maximumacceleration ability opposite to the current direction of movementand/or orientation of the living being (2) is predefined.
 41. The methodaccording to claim 40, characterized in that at least one of thefollowing parameters is predefined for the living being (2): a maximumspeed from which the acceleration ability in the current direction ofmovement is zero, a maximum acceleration in the direction of orientationof a non-moving living being (2) as well opposite to said orientation, aspeed at which the maximum acceleration ability in the current directionof movement is highest, a speed at which the maximum accelerationability opposite to the current direction of movement and/or orientationof the living being (2) is highest in value, a maximum speed opposite tothe orientation of the living being (2) from which the accelerationability opposite to said orientation is zero, wherein these values arepreferably predefined as a function of the class of living being (2)concerned, in particular varying according to age, gender and bodydimensions.
 42. The method according to claim 33, characterized in thata minimum possible curve radius, which is dependent on the currentwalking speed and/or acceleration, is taken into account for thedetermination of the potential future position.
 43. The method accordingto claim 33, characterized in that a maximum deceleration ability, whichis dependent on the speed of movement and/or a curve radius of themovement made by the living being (2), is taken into account for thedetermination of the potential future position.
 44. The method accordingto claim 33, characterized in that an angle at which the living being(2) is positioned or moves relative to a path of travel of the vehicle(1) is taken into account for the determination of the potential futureposition, wherein said angle is used to determine the amount of time ittakes the living being (2) to turn towards the path of travel whileaccelerating substantially at the same time in order to reach the travelpath area.
 45. The method according to claim 44, characterized in thatthe angle taken into account is an angle ranging between 150° and 210°,thus taking into account a living being (2) that is positioned or moveswith his/her back to the path of travel.
 46. The method according toclaim 44, characterized in that the angle taken into account is an angleranging between 60° and 120°, thus taking into account a living being(2) that is positioned or moves with his/her side to the path of travel.47. The method according to claim 33, characterized in that thepotential future position is determined taking into account a relativeposition of the living being (2) to a path of travel, in particular adistance at which the living being (2) is positioned or moves relativeto said path of travel, wherein said relative position is used todetermine the amount of time it takes the living being (2) a to speed upin order to reach the travel path area.
 48. The method according toclaim 33, characterized in that surroundings information and/orobstacles are taken into account for the determination of the potentialfuture position.
 49. The method according to claim 1, wherein before thevehicle (200) is put into operation, a finite number of typical initialsituations of motion (BSi(vi, ai, wi)) for different types ofpedestrians (100) are measured and stored in a memory which is locatedaboard the vehicle (200).
 50. The method according to claim 49, whereina group (TSi) of potential movement trajectories (Ti1, Ti2, . . . ,Ti10) is computed for a predefined period of time comprising increments(Δt) for each of these initial situations of motion (BSi(vi, ai, wi)).51. The method according to claim 50, wherein the initial situations ofmotion (BSi(vi, ai, wi)) and the trajectory groups (TSi) that have beencomputed are stored in the memory aboard the vehicle.
 52. The methodaccording to claim 51, wherein the risk of a collision is computed withthe following method steps during operation of the vehicle: the state ofmotion of the pedestrian (100) is detected using a suitable sensorsystem; the nearest initial situation of motion (BSi(vi, ai, wi)) of thepedestrian (100), which was measured and stored in the memory before thevehicle (200) was put into operation, is selected; and the trajectorygroup (TSi) which was computed for the selected initial situation ofmotion (BSi(vi, ai, wi)) before the vehicle (200) was put into operationand is stored with reference to said initial situation of motion(BSi(vi, ai, wi)) is retrieved and placed around the position of thepedestrian (100) that has been detected, in accordance with theorientation of said pedestrian (100).
 53. The method according to claim52, wherein the risk of a collision is further computed with thefollowing method steps: the travel of the vehicle is extrapolated atsmall time increments, thus obtaining a driving path (210), wherein saiddriving path (210) comprises collision zones (221, 222, 223, 224) atrespective time increments (t1, t2, t3, t4); at each time increment (t1,t2, t3, t4), only the position points (p10, . . . , p19; p20, . . . ,p29; p30, . . . , p39; p40, . . . , p49) of the trajectories (Ti1, Ti2,. . . , Ti10) of the selected trajectory group (TSi) are analyzed,wherein said position points (p10, . . . , p19; p20, . . . , p29; p30, .. . , p39; p40, . . . , p49) at the respective time increment (t1, t2,t3, t4) reflect the potential positions of the pedestrian (100) at saidtime increment (t1, t2, t3, t4); next, it is checked whether theselected position m points (p10, . . . , p19; p20, . . . , p29; p30, . .. , p39; p40, . . . , p49) are located within the collision zone (221,222, 223, 224) of the vehicle (200); and the number of trajectories(Ti1, Ti2, Ti3, Ti10) within the trajectory group (TSi) comprising theposition points (p10, p21, p29, p32) which are located within one of thecollision zones (221, 222, 223, 224) of the vehicle (200) and predict asingle collision between the vehicle (200) and the pedestrian (100) isdetermined.
 54. The method according to claim 53, wherein thosetrajectories (Ti1, Ti2, Ti3, Ti10) comprising the position points (p10,p21, p29, p32) which are located within one of the collision zones (221,222, 223, 224) of the vehicle (200) and predict a single collisionbetween the vehicle (200) and the pedestrian (100) are disregarded inthe subsequent computation steps for the next time increments (t2, t3,t4).
 55. The method according to claim 52, wherein the method steps arerepeated at time increments (Δt) in order to determine the number oftrajectories (Ti1, Ti2, Ti3, Ti10) comprising the position points (p10,p21, p29, p32) which are located within one of the collision zones (221,222, 223, 224) of the vehicle (200) and predict a single collisionbetween the vehicle (200) and the pedestrian (100) for the subsequenttime increments (t2, t3, t4).
 56. The method according to claim 52,wherein the method steps are continued to be carried out until thevehicle (200) has passed the pedestrian (100) to an extent that nofurther collisions between the vehicle (200) and the pedestrian (100)may occur.
 57. The method according to claim 56, wherein the totalnumber of trajectories (Ti1, Ti2, Ti3, Ti10) comprising at least oneposition point (p10, p21, p29, p32) which is located within one of thecollision zones (221, 222, 223, 224) of the vehicle (200) is determined,and the quotient (Q) of the total number of collision trajectories (Ti1,Ti2, Ti3, Ti10) and the total number of trajectories (Ti1, . . . , Ti10)is computed.
 58. A vehicle comprising a protection system for livingbeings (2) outside said vehicle (1), in particular pedestrian protectiondevices comprising at least one sensing system to obtain surroundingsinformation, comprising a computing unit which evaluates saidsurroundings information in order to identify a living being, inparticular a pedestrian (2), determines a movement trajectory for eachof the living being (2) and the vehicle (1) as a trajectory pair, anduses said trajectory pair to deduce the probability of a collision andhence the necessity to activate a protection system, wherein the sensingsystem is designed to detect parameters of living beings (2) and oftheir physiological movement ability, and the computing unit is designedto determine the potential future position at a given moment in time,based on a location of the movement trajectory and on the state ofmotion and taking into account a physiological movement ability of theliving being (2) for one or several future moments in time.